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Record W4386600587 · doi:10.1016/j.ccell.2023.08.007

Unfolding the secrets of small cell lung cancer progression: Novel approaches and insights through rapid autopsies

2023· article· en· W4386600587 on OpenAlexfundno aff
Zsolt Megyesfalvi, Simon Heeke, Benjamin J. Drapkin, Anna Solta, Ildikó Kovács, Kristiina Boettiger, Lilla Horváth, Büsra Ernhofer, János Fillinger, F Rényi-Vámos, Clemens Aigner, Karin Schelch, Christian Lang, György Marko‐Varga, Carl M. Gay, Lauren A. Byers, Benjamin B. Morris, John V. Heymach, Peter Van Loo, Fred R. Hirsch, Balázs Döme

Bibliographic record

VenueCancer Cell · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsnot available
FundersCancer MoonshotNational Cancer InstituteNational Research, Development and Innovation OfficeUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthInternational Association for the Study of Lung CancerHungarian-American Enterprise Scholarship FundFru Berta Kamprads StiftelseSierra OncologyInnovációs és Technológiai MinisztériumMagyar Tudományos AkadémiaBeiGeneMagyar Tüdőgyógyász TársaságCancer Prevention and Research Institute of TexasAustrian Science FundMoonshot Research and Development ProgramAstraZenecaBristol-Myers Squibb
KeywordsBiologyLung cancerCancerCancer researchCellComputational biologyMedicinePathologyGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.096
GPT teacher head0.358
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractno

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